Cover Image

How Effective Is Constitutional AI in Small LLMs? A Study on DeepSeek-R1 and Its Peers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Menke, Antonio-Gabriel Chacón, Tan, Phan Xuan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Annotating the Chain-of-Thought: A Behavior-Labeled Dataset for AI Safety
by: Menke, Antonio-Gabriel Chacón, et al.
Published: (2025)

DeepSeek reshaping healthcare in China's tertiary hospitals
by: Chen, Jishizhan, et al.
Published: (2025)

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
by: DeepSeek-AI, et al.
Published: (2025)

Bridging Technology and Humanities: Evaluating the Impact of Large Language Models on Social Sciences Research with DeepSeek-R1
by: Gu, Peiran, et al.
Published: (2025)

Safety Evaluation and Enhancement of DeepSeek Models in Chinese Contexts
by: Zhang, Wenjing, et al.
Published: (2025)

Benchmark-Driven Selection of AI: Evidence from DeepSeek-R1
by: Spelda, Petr, et al.
Published: (2025)

Emotion-Aware Embedding Fusion in LLMs (Flan-T5, LLAMA 2, DeepSeek-R1, and ChatGPT 4) for Intelligent Response Generation
by: Rasool, Abdur, et al.
Published: (2024)

Can AI be a Teaching Partner? Evaluating ChatGPT, Gemini, and DeepSeek across Three Teaching Strategies
by: de Souza, Talita de Paula Cypriano, et al.
Published: (2026)

A Comparison of DeepSeek and Other LLMs
by: Gao, Tianchen, et al.
Published: (2025)

Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek
by: Qiu, Peiran, et al.
Published: (2025)

Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies
by: Parmar, Manojkumar, et al.
Published: (2025)

Quantitative Analysis of Performance Drop in DeepSeek Model Quantization
by: Zhao, Enbo, et al.
Published: (2025)

User Intent to Use DeepSeek for Healthcare Purposes and their Trust in the Large Language Model: Multinational Survey Study
by: Choudhury, Avishek, et al.
Published: (2025)

Is Power-Seeking AI an Existential Risk?
by: Carlsmith, Joseph
Published: (2022)

Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis
by: Zhao, Kaikai, et al.
Published: (2025)

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
by: DeepSeek-AI, et al.
Published: (2024)

o3-mini vs DeepSeek-R1: Which One is Safer?
by: Arrieta, Aitor, et al.
Published: (2025)

Public Constitutional AI
by: Abiri, Gilad
Published: (2024)

DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks
by: Jiang, Qile, et al.
Published: (2025)

DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey
by: Qiao, Yu, et al.
Published: (2025)

DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
by: DeepSeek-AI, et al.
Published: (2024)

DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models
by: Xiong, Luolin, et al.
Published: (2025)

Output Length Effect on DeepSeek-R1's Safety in Forced Thinking
by: Li, Xuying, et al.
Published: (2025)

Towards Effective Discrimination Testing for Generative AI
by: Zollo, Thomas P., et al.
Published: (2024)

LLMs in Disease Diagnosis: A Comparative Study of DeepSeek-R1 and O3 Mini Across Chronic Health Conditions
by: Gupta, Gaurav Kumar, et al.
Published: (2025)

Mixture of Tunable Experts -- Behavior Modification of DeepSeek-R1 at Inference Time
by: Dahlke, Robert, et al.
Published: (2025)

DeepSeek-V3 Technical Report
by: DeepSeek-AI, et al.
Published: (2024)

Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review
by: Sharma, Vibhhu, et al.
Published: (2026)

Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3
by: Sadik, Ahmed R., et al.
Published: (2025)

How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare
by: Schroeder, Daniel Thilo, et al.
Published: (2025)

ChatGPT vs. DeepSeek: A Comparative Study on AI-Based Code Generation
by: Manik, Md Motaleb Hossen
Published: (2025)

DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
by: Xin, Huajian, et al.
Published: (2024)

RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning Capability
by: Zhang, Yichi, et al.
Published: (2025)

Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons
by: So, Chi Chiu, et al.
Published: (2025)

Explainable AI for Mental Health Emergency Returns: Integrating LLMs with Predictive Modeling
by: Ahmed, Abdulaziz, et al.
Published: (2025)

Explainable AI Systems Must Be Contestable: Here's How to Make It Happen
by: Moreira, Catarina, et al.
Published: (2025)

Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning
by: Huang, Donghao, et al.
Published: (2025)

DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
by: Xin, Huajian, et al.
Published: (2024)

From Protoscience to Epistemic Monoculture: How Benchmarking Set the Stage for the Deep Learning Revolution
by: Koch, Bernard J., et al.
Published: (2024)

On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions
by: Nguyen, Dang, et al.
Published: (2025)